agentleFS
ArangoGutierrezArangoGutierrez/claude-toolkit/.claude/skills/done/SKILL.md

done

Skill4 starsChanged 3 months agoMITView on GitHub

Confirm or abandon the session goal with NAT-backed evidence evaluation. Triggered by /done, /done confirm, /done abandon <reason>, /done amend <text>, /done override [reason].

  • Reads credentials

We split this skill into the rules it gives an agent

2 rules. 0 are common, 0 are distinctive, and 2 are very specific to this project.

  • 0

    Common

    Most of the 32 edited versions we read keep these.

  • 0

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    When the user wants to record ABANDONED <reason> or amend the goal.

    line 17
  2. Very specific:

    Default model: DONE_NAT_MODEL env if set, else the public catalog ID nvidia/nemotron-3-ultra-550b-a55b:free.

    line 43

“Common” is measured: kept by at least 6 in 10 of the 32 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

/done

Surfaces a candidate verdict against the captured session goal using a NAT-backed goal-evaluator panelist, and writes the authoritative user.verdict into the daily outcomes log.

When to use

  • End of a session, when the user believes the goal has been met (or not).
  • When the user wants to record ABANDONED <reason> or amend the goal.

Subcommands

SubcommandBehavior
/done or /done confirmRead latest outcomes evidence + last goal stanza. Invoke NAT goal-evaluator. AGREE → write user.verdict=MET. DISAGREE → surface NAT rationale; ask user to override or amend. INSUFFICIENT → ask user for explicit verdict using NAT's GAPS. NAT ERROR → fall through to user_only.
/done abandon <reason>Skip NAT call. Write user.verdict=ABANDONED with reason=<reason>.
/done amend <text>Forward to /goal amend <text>; no outcomes entry written.
/done override [reason]Skip NAT call. Write user.verdict=MET with evaluator=user_override, nat_verdict=OVERRIDDEN, and reason=<reason> (default "user override" if omitted).

override exists because the user is authoritative and NAT is advisory: when NAT's DISAGREE or INSUFFICIENT_EVIDENCE verdict is wrong (e.g. it misread evidence), the user needs a way to close the goal without either being stuck in a confirm loop or having to lie about NAT's verdict — override records the disagreement honestly via evaluator=user_override / nat_verdict=OVERRIDDEN, it does not fake an AGREE.

Implementation

The skill runs ~/.claude/skills/done/done.sh (and eval.py) via Python 3.12. Run with the sandbox disabled — done.sh writes the outcomes log under ~/.claude/audit/, which the Bash sandbox blocks. UUID resolution matches goal.sh ($CLAUDE_SESSION_ID → $CLAUDE_CODE_SESSION_ID → $$.json → newest-file-with-warning), so /done grades the right session. The Python module mirrors the validate-recommendation v3 panel/dispatch.py pattern: a single mockable _invoke_nat seam and ERROR-fallback wrapping so all NAT/HTTP/parse failures degrade gracefully to user_only.

NAT model

Default model: DONE_NAT_MODEL env if set, else the public catalog ID nvidia/nemotron-3-ultra-550b-a55b:free.

Backend: DONE_BACKEND selects the provider — nat-nim (default) or nat-openai for any OpenAI-compatible endpoint, including OpenRouter. Endpoint resolution: DONE_NAT_ENDPOINT if set, else (for nat-openai) OPENAI_BASE_URL.

Auth key resolution: DONE_NAT_API_KEY if set, else (for nat-openai) OPENAI_API_KEY.

OpenRouter :free routes may log prompts for provider training; use a paid route or a self-hosted endpoint for sensitive work.

Private hub-specific details — internal model ID forms, the nat-nim panel-fallback endpoint/key chain, and the internal endpoint host — live in docs/nat-hub-endpoints.md and are never extracted to the public toolkit.

Spec

docs/superpowers/specs/2026-05-18-done-hook-design.md §Component 5.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.